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A Study on Integrating Crypto-Stego Techniques to Minimize the Distortion
Abstract
Now a days on-line services has end up as a big part of our lives that performs communication electronically. This e-communication requires confidentiality and information integrity to protect from unauthorized users. Security can be provided by using two widespread techniques i.e., cryptography and steganography. However, no single technique in itself is well acquainted to cater to our needs in implementing a secure, and robust system. Steganography helps in hiding the data behind the cover image. But once known secret message can be captured very easily. Moreover, by embedding the data into the image medium, we increase the risk of distortion. If the distorted image is sent over the communication channel, the intruder can easily guess that some secret data has been sent and that secret data can be easily recovered. As we know, cryptography ciphers the text so that the secret data sent over the communication link cannot be understood. But if both the security mechanisms can be integrated we can come up with a system which is more secure and robust. This paper presents the study of hybrid crypto-stego techniques so as to reduce distortion by maintaining the imperceptibility, robustness and providing high security for the e-communication between two authorized parties
... The development of a universal neural network expedites the deployment of natural language processing systems and streamlines the model-building process. In order to create complex representations that can be used to a range of natural language processing tasks, deep learning models must be able to identify intricate links and linkages in the input data [25]. Their adaptability may be attributed, in part, to the unified neural network architecture's design principles, which prioritize generalization and abstraction above task-specific optimization [22]. ...
... Researchers working on natural language processing (NLP) systems might offer fresh approaches to information analysis and retrieval. These improvements might include contextual information, domain-specific expertise, and trustworthy disambiguation algorithms [25][26][27][28][29][30]. ...
The paper presents the state-of-the-art natural language processing (NLP) models and methods, such as BERT and DistilBERT, to evaluate textual data and extract noteworthy insights. Preprocessing textual input, tokenization, and the implementation of deep learning architectures such as bidirectional LSTMs for classification tasks are all components of the approach that has been presented. To achieve the goal of producing accurate prediction models with the least amount of validation loss possible. Natural language processing (NLP) is a major focus of the manuscript in multiple areas such as sentiment analysis, language understanding, and text classification. The results show that our proposed NLP models perform exceptionally well. Long-term memory and natural language processing (NLP) go hand in hand. Therefore, these results demonstrate the value and relevance of our natural language processing approach to obtaining unstructured text data to improve and develop a variety of applications, such as chatbots, virtual assistants, and information retrieval systems, as well as to gain insights and help make better decisions, and the flexibility and generalizability of the models, while confirming their ability to handle a range of activities and textual materials. Excellent and accurate results were obtained in terms of validation, with the experimental models often exceeding the 99.85% accuracy benchmark. Another crucial factor to consider is that the average validation loss metrics for all tests remained remarkably low at 0.0058.
... These metrics tell about the quality, accuracy, and generalisation aspect of models in regression tasking [11,[27][28][29][30][31]. 6. Analysis of Results: The analysis will be done to investigate the strong and weak points of the QML models in regression. The model's performance on the different regression jobs can be analysed for discerning patterns and trends in the results, and the implications of the findings for the future research and development in the discipline can be discussed [17,[32][33][34][35][36]. ...
Quantum machine learning (QML) can be employed in solving complicated machine learning tasks although the performance in examining the regression processes is only barely understood. Knowledge gaps are intended to be closed by studying modelling performance of QML in regression tasks, with emphasis being dedicated to scaling up and ability to resist noise. The regression part offers the following functions that include straight line and complex operations. Furthermore, the authors employ quantum neural networks generated using Qiskit to perform experiments. The results demonstrate that QML has a remarkable level of accuracy in basic regressions, reaching a maximum of 97%. Nevertheless, there are difficulties in representing intricate functions, such as 5 × cos(x), which results in a noticeable decline in performance. The work deals with the influence of noise and IERs from imperfect hardware on the efficiency of QML algorithms providing insight into the core obstacles. The result of a detailed examination of the results that have tested the powers and limits of QML in the development of regression applications is represented. The future direction of research and development will be defined by the results obtained in it.
Different welding speed forces and tool temperature are accurately recorded in standard welding. An arbitrary activated number is generated based on the (ALE) formula. The main feature of the numerical approach is to accurately calculate the contact and friction surface between the plate and the tool. The first study using Norton's friction model showed the high sensitivity of welding forces to the possibility of obtaining tool temperature for friction coefficients. It takes into account changes caused by small frictional variations. It is impossible to make very precise calibrations on the forces and provide an accurate instrument temperature profile. On the other hand, use the Columbine friction model to obtain realistic temperature profiles. This allows you to measure the coefficient of friction. This provides excellent agreement with experiments.
The ECG records these stimuli to show how fast the heart beats, rhythm (constant or irregular) and the intensity and duration of the heartbeat the current impulse going through different parts of the heart. An electrocardiogram Sensors that are Each time your heart is Replacing the simple test.ECG may be a sign of multiple heart failure.-Related Terms. Your cholesterol level can be measured by a blood test that includes LDL "bad" cholesterol and HDL "good" cholesterol. In general, a lower heart rate during rest indicates a higher performance heart rate and better heart rate exercise. This will help diagnose other conditions. It can affect your heart like anemia or thyroid disease. A normal sober heartbeat for adults is 60 to 100 beats per minute. It is used to diagnose symptoms of possible heart problems such as chest pain and palpitations and shortness of breath and a sudden blockage in the blood supply to the heart. Coronary heart disease-Obstructs or blocks the blood supply to the heart due to the formation of fatty substances. An ECG can help diagnose: Arrhythmia-The heart is beating too fast, too fast or irregularly. The ECG has its limitations: the ECG only reveals the heartbeat and rhythm, which may take a few seconds to record. If intermittent arrhythmia occurs, the ECG may not be taken and further monitoring may be required.
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